simulateData {ccems}R Documentation

Simulate Data

Description

This function generates expected values of responses at the total concentrations of the dataframe g$d or at points specified in predict if predict is not NULL.

Usage

 simulateData(g, init = FALSE, predict = NULL, typeYP = NULL) 

Arguments

g A specific model/hypothesis created by mkModel.
init This is TRUE only in first calls to this function by the parameter estimate optimization algorithm. When TRUE the initial AIC value is set.
predict A dataframe of total concentrations of the reactants at which response predictions are desired.
typeYP The type of output data desired for the predictions. Options are "m" and "v" for average mass and reaction velocity, respectively.

Details

This function is the workhorse core of the nonlinear least squares algorithm, so its speed is critical which is why it uses compiled C code when g$TCC = TRUE. In addition to model fitting, this function can also be used to predict system response surfaces over grids of physiologically relevant total concentrations of the reactants. It is assumed that the model used to formulate predictions is based on data, and that the output type of the predictions is the same as the output type used to build the model.

Value

The input model object augmented to include the following fields if predict is NULL.

echk A matrix that checks the TCC solver and g$fback. Matrix column names that end in Q should match their sans-Q counterparts.
eSS The expected steady state concentrations of species (complexes and free reactants). For each row of the data dataframe there is a row in this matrix. Its contents are the TCC solver solution (free reactant expected concentrations) and the result of applying g$fback to them to create expected complex concentrations.
res The residuals of the fit.
nData The number of data points (i.e. rows) in the data dataframe g$d.
SSE The initial and final sum of squared errors (i.e. residual sum of squares).
AIC The initial and final Akaike Information Criterion values, corrected for small samples. S ince nonlinear least squares is used AIC = N*log(SSE/N)+2*P + 2*P*(P+1)/(N-P-1) + N*log(2*pi) + N where N = nData and P is the number of estimated parameters (including the variance).
predict The input argument predict with an additional expected system response column named "EY".

Note

The function fitModel augments the input model object by the same six fields above because it calls this function iteratively.

Measurements are often made at total concentrations that are substantially higher than physiological values due to signal-to-noise limitations. Thus, predictions in physiologically relevant (and thus important) regions tend to be weak.

This work was supported by the National Cancer Institute (K25CA104791).

Author(s)

Tom Radivoyevitch (txr24@case.edu)

References

Radivoyevitch, T. (2008) Equilibrium model selection: dTTP induced R1 dimerization. BMC Systems Biology 2, 15.

See Also

The experimental design example expDesign in the docs directory.

Examples

library(ccems)
topology <- list(  
        heads=c("R1t0","R2t0"),  
        sites=list(       
                s=list(                     # s-site    thread #
                        m=c("R1t1"),        # monomer      1
                        d=c("R2t1","R2t2")  # dimer        2
                )
        )
) 
g <- mkg(topology,TCC=TRUE) 
d=subset(RNR,(year==2001)&(fg==1)&(t>0)&(G==0),select=c(R,t,m,year))
names(d)[1:2] <-c("RT","tT") 
mdl=mkModel(g,"IIIJ",d,Kjparams=c(R2t0=Inf, R1t1=Inf,R2t1=Inf, R2t2=1),pparams=c(p=1))
fmdl <- fitModel(mdl)
pt=c(.1,1:20)
predict <- data.frame(RT = rep(7.6,length(pt)), tT = pt)
df <- simulateData(fmdl,predict=predict,typeYP="m")$predict  
plot(d$tT,d$m,type="p",  xlab="[dTTP] (uM)", ylab="Weight averaged R1 mass", 
     main="Scott et al. Biochemistry, 2001, Fig. 1 (DLS data)")
lines(df$tT,df$EY) 


[Package ccems version 1.02 Index]